As a completely different application in multiple respects—object count,
feature count and type of measurements—we tested Algorithm
REF  with the classic iris flower data
set[1]} with the correction of [2]}. It turns out
that Algorithm REF  performs well on this data set despite
of its small number of objects and features. The following results have all
been obtained using \(\mathsf {nb}=5\)  or 6, 60%/1% positive/negative training
set size and specifying columns #3 and #4 as Cics. The selection of Cics
can also be done by prescribing \(b^{+}\)  (see Algorithm REF ),
with slightly worse quality of prediction.
<TABLE>